Overview

Dataset statistics

Number of variables18
Number of observations682
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory86.7 KiB
Average record size in memory130.2 B

Variable types

Numeric13
Categorical3
Boolean2

Alerts

amount is highly correlated with duration and 1 other fieldsHigh correlation
duration is highly correlated with amountHigh correlation
payments is highly correlated with amount and 1 other fieldsHigh correlation
Average_order_amount is highly correlated with payments and 1 other fieldsHigh correlation
Average_trans_amount is highly correlated with Average_order_amount and 1 other fieldsHigh correlation
Average_trans_balance is highly correlated with Average_trans_amountHigh correlation
No_inhabitants is highly correlated with Average_salary and 1 other fieldsHigh correlation
Average_salary is highly correlated with No_inhabitants and 1 other fieldsHigh correlation
Average_crime_rate is highly correlated with No_inhabitants and 1 other fieldsHigh correlation
amount is highly correlated with duration and 1 other fieldsHigh correlation
duration is highly correlated with amountHigh correlation
payments is highly correlated with amount and 1 other fieldsHigh correlation
Average_order_amount is highly correlated with payments and 1 other fieldsHigh correlation
Average_trans_amount is highly correlated with Average_order_amount and 1 other fieldsHigh correlation
Average_trans_balance is highly correlated with Average_trans_amountHigh correlation
No_inhabitants is highly correlated with Average_salary and 1 other fieldsHigh correlation
Average_salary is highly correlated with No_inhabitants and 1 other fieldsHigh correlation
Average_crime_rate is highly correlated with No_inhabitants and 1 other fieldsHigh correlation
amount is highly correlated with paymentsHigh correlation
payments is highly correlated with amount and 1 other fieldsHigh correlation
Average_order_amount is highly correlated with paymentsHigh correlation
Average_trans_amount is highly correlated with Average_trans_balanceHigh correlation
Average_trans_balance is highly correlated with Average_trans_amountHigh correlation
No_inhabitants is highly correlated with Average_salaryHigh correlation
Average_salary is highly correlated with No_inhabitants and 1 other fieldsHigh correlation
Average_crime_rate is highly correlated with Average_salaryHigh correlation
amount is highly correlated with duration and 3 other fieldsHigh correlation
duration is highly correlated with amountHigh correlation
payments is highly correlated with amount and 2 other fieldsHigh correlation
Average_order_amount is highly correlated with amount and 2 other fieldsHigh correlation
Average_trans_amount is highly correlated with amount and 3 other fieldsHigh correlation
Average_trans_balance is highly correlated with Average_trans_amountHigh correlation
No_transaction is highly correlated with Same_district and 1 other fieldsHigh correlation
No_inhabitants is highly correlated with Average_salary and 2 other fieldsHigh correlation
Average_salary is highly correlated with No_inhabitants and 2 other fieldsHigh correlation
Average_unemployment_rate is highly correlated with No_inhabitants and 2 other fieldsHigh correlation
Average_crime_rate is highly correlated with No_inhabitants and 2 other fieldsHigh correlation
Same_district is highly correlated with No_transactionHigh correlation
Default is highly correlated with No_transactionHigh correlation
account_id has unique values Unique
Average_trans_amount has unique values Unique
Average_trans_balance has unique values Unique

Reproduction

Analysis started2022-04-13 09:23:06.035323
Analysis finished2022-04-13 09:23:45.461534
Duration39.43 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

account_id
Real number (ℝ≥0)

UNIQUE

Distinct682
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5824.162757
Minimum2
Maximum11362
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.5 KiB
2022-04-13T14:53:45.570730image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile677.15
Q12967
median5738.5
Q38686
95-th percentile10811.75
Maximum11362
Range11360
Interquartile range (IQR)5719

Descriptive statistics

Standard deviation3283.512681
Coefficient of variation (CV)0.5637741969
Kurtosis-1.214471838
Mean5824.162757
Median Absolute Deviation (MAD)2847.5
Skewness-0.04138031497
Sum3972079
Variance10781455.53
MonotonicityNot monotonic
2022-04-13T14:53:45.711391image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
52701
 
0.1%
75651
 
0.1%
104781
 
0.1%
21871
 
0.1%
7181
 
0.1%
1031
 
0.1%
6661
 
0.1%
78901
 
0.1%
29361
 
0.1%
100651
 
0.1%
Other values (672)672
98.5%
ValueCountFrequency (%)
21
0.1%
191
0.1%
251
0.1%
371
0.1%
381
0.1%
671
0.1%
971
0.1%
1031
0.1%
1051
0.1%
1101
0.1%
ValueCountFrequency (%)
113621
0.1%
113591
0.1%
113491
0.1%
113281
0.1%
113271
0.1%
113171
0.1%
112711
0.1%
112651
0.1%
112441
0.1%
112311
0.1%

amount
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct645
Distinct (%)94.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean151410.176
Minimum4980
Maximum590820
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.5 KiB
2022-04-13T14:53:45.852017image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum4980
5-th percentile22972.2
Q166732
median116928
Q3210654
95-th percentile388365.6
Maximum590820
Range585840
Interquartile range (IQR)143922

Descriptive statistics

Standard deviation113372.4063
Coefficient of variation (CV)0.7487766631
Kurtosis0.8378338014
Mean151410.176
Median Absolute Deviation (MAD)67650
Skewness1.114209923
Sum103261740
Variance1.285330251 × 1010
MonotonicityNot monotonic
2022-04-13T14:53:45.992605image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
302764
 
0.6%
861843
 
0.4%
2653202
 
0.3%
2325602
 
0.3%
674642
 
0.3%
1747442
 
0.3%
2722202
 
0.3%
1556162
 
0.3%
1254722
 
0.3%
493202
 
0.3%
Other values (635)659
96.6%
ValueCountFrequency (%)
49801
0.1%
51481
0.1%
76561
0.1%
86161
0.1%
109441
0.1%
114001
0.1%
117361
0.1%
125401
0.1%
127921
0.1%
140281
0.1%
ValueCountFrequency (%)
5908201
0.1%
5666401
0.1%
5412001
0.1%
5385001
0.1%
5040001
0.1%
4951801
0.1%
4829401
0.1%
4756801
0.1%
4732801
0.1%
4680601
0.1%

duration
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct5
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size5.5 KiB
60
145 
24
138 
48
138 
12
131 
36
130 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row24
2nd row12
3rd row36
4th row12
5th row12

Common Values

ValueCountFrequency (%)
60145
21.3%
24138
20.2%
48138
20.2%
12131
19.2%
36130
19.1%

Length

2022-04-13T14:53:46.133231image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-13T14:53:46.211356image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
60145
21.3%
24138
20.2%
48138
20.2%
12131
19.2%
36130
19.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

payments
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct577
Distinct (%)84.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4190.664223
Minimum304
Maximum9910
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.5 KiB
2022-04-13T14:53:46.320731image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum304
5-th percentile963.9
Q12477
median3934
Q35813.5
95-th percentile8048.2
Maximum9910
Range9606
Interquartile range (IQR)3336.5

Descriptive statistics

Standard deviation2215.830344
Coefficient of variation (CV)0.5287539699
Kurtosis-0.6975693672
Mean4190.664223
Median Absolute Deviation (MAD)1688
Skewness0.3543746397
Sum2858033
Variance4909904.115
MonotonicityNot monotonic
2022-04-13T14:53:46.476981image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
25234
 
0.6%
31514
 
0.6%
43633
 
0.4%
38743
 
0.4%
23073
 
0.4%
53543
 
0.4%
73703
 
0.4%
27793
 
0.4%
36983
 
0.4%
45373
 
0.4%
Other values (567)650
95.3%
ValueCountFrequency (%)
3041
0.1%
3121
0.1%
3191
0.1%
3341
0.1%
3591
0.1%
3711
0.1%
4031
0.1%
4151
0.1%
4241
0.1%
4291
0.1%
ValueCountFrequency (%)
99101
0.1%
98471
0.1%
97361
0.1%
97211
0.1%
96981
0.1%
96891
0.1%
94441
0.1%
92681
0.1%
91121
0.1%
90201
0.1%

DID
Real number (ℝ≥0)

Distinct400
Distinct (%)58.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean398.2404692
Minimum102
Maximum697
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.5 KiB
2022-04-13T14:53:46.633230image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum102
5-th percentile136.05
Q1261.25
median395.5
Q3528.75
95-th percentile664
Maximum697
Range595
Interquartile range (IQR)267.5

Descriptive statistics

Standard deviation164.611359
Coefficient of variation (CV)0.4133466378
Kurtosis-1.058438529
Mean398.2404692
Median Absolute Deviation (MAD)134
Skewness0.02740908412
Sum271600
Variance27096.89951
MonotonicityNot monotonic
2022-04-13T14:53:46.773883image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5616
 
0.9%
3335
 
0.7%
3134
 
0.6%
4404
 
0.6%
4154
 
0.6%
2544
 
0.6%
3044
 
0.6%
1254
 
0.6%
6844
 
0.6%
2394
 
0.6%
Other values (390)639
93.7%
ValueCountFrequency (%)
1021
0.1%
1031
0.1%
1052
0.3%
1071
0.1%
1082
0.3%
1101
0.1%
1111
0.1%
1141
0.1%
1152
0.3%
1171
0.1%
ValueCountFrequency (%)
6971
 
0.1%
6961
 
0.1%
6941
 
0.1%
6933
0.4%
6912
0.3%
6892
0.3%
6861
 
0.1%
6851
 
0.1%
6844
0.6%
6831
 
0.1%

Average_order_amount
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct651
Distinct (%)95.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4486.950975
Minimum312
Maximum10817
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.5 KiB
2022-04-13T14:53:47.164515image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum312
5-th percentile1537.4125
Q12823.516667
median4193.616667
Q35863.05
95-th percentile8252.44
Maximum10817
Range10505
Interquartile range (IQR)3039.533333

Descriptive statistics

Standard deviation2134.790905
Coefficient of variation (CV)0.475777631
Kurtosis-0.4391549935
Mean4486.950975
Median Absolute Deviation (MAD)1500.533333
Skewness0.5143048559
Sum3060100.565
Variance4557332.21
MonotonicityNot monotonic
2022-04-13T14:53:47.305141image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5353.53
 
0.4%
3162.52
 
0.3%
8033.22
 
0.3%
3475.1666672
 
0.3%
3216.72
 
0.3%
18602
 
0.3%
48452
 
0.3%
3151.32
 
0.3%
3252.52
 
0.3%
5577.32
 
0.3%
Other values (641)661
96.9%
ValueCountFrequency (%)
3121
0.1%
5191
0.1%
5291
0.1%
7081
0.1%
763.51
0.1%
811.61
0.1%
846.61
0.1%
8501
0.1%
1041.51
0.1%
10541
0.1%
ValueCountFrequency (%)
108171
0.1%
103701
0.1%
10054.851
0.1%
10038.151
0.1%
9967.651
0.1%
9794.51
0.1%
97211
0.1%
96891
0.1%
96791
0.1%
9354.41
0.1%

Average_trans_amount
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct682
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8460.899856
Minimum1315.619048
Maximum19772.47368
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.5 KiB
2022-04-13T14:53:47.461390image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1315.619048
5-th percentile3094.60153
Q15490.948511
median8331.56015
Q311346.23043
95-th percentile14409.20299
Maximum19772.47368
Range18456.85464
Interquartile range (IQR)5855.281915

Descriptive statistics

Standard deviation3664.134172
Coefficient of variation (CV)0.4330667227
Kurtosis-0.7154212983
Mean8460.899856
Median Absolute Deviation (MAD)2947.734174
Skewness0.2338828596
Sum5770333.702
Variance13425879.23
MonotonicityNot monotonic
2022-04-13T14:53:47.602015image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15016.194811
 
0.1%
6117.4017861
 
0.1%
11961.103631
 
0.1%
13587.450451
 
0.1%
9901.7366551
 
0.1%
9268.9621621
 
0.1%
3887.4420291
 
0.1%
2988.1764711
 
0.1%
4320.0552761
 
0.1%
15935.49811
 
0.1%
Other values (672)672
98.5%
ValueCountFrequency (%)
1315.6190481
0.1%
1522.2874021
0.1%
1540.1150441
0.1%
1556.7817371
0.1%
1894.1631211
0.1%
2002.9944851
0.1%
2022.0544221
0.1%
2054.3052961
0.1%
2073.111
0.1%
2147.041
0.1%
ValueCountFrequency (%)
19772.473681
0.1%
17747.789471
0.1%
17625.8251
0.1%
17554.810341
0.1%
17553.509431
0.1%
17180.596391
0.1%
17135.36171
0.1%
16946.405231
0.1%
16527.975381
0.1%
16430.536591
0.1%

Average_trans_balance
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct682
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean44935.27845
Minimum6690.547112
Maximum79272.1245
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.5 KiB
2022-04-13T14:53:47.742645image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum6690.547112
5-th percentile20689.56502
Q134685.44948
median45614.40178
Q355426.2194
95-th percentile67146.79671
Maximum79272.1245
Range72581.57739
Interquartile range (IQR)20740.76992

Descriptive statistics

Standard deviation14082.25188
Coefficient of variation (CV)0.3133896655
Kurtosis-0.6005487629
Mean44935.27845
Median Absolute Deviation (MAD)10513.49402
Skewness-0.111166485
Sum30645859.91
Variance198309818.1
MonotonicityNot monotonic
2022-04-13T14:53:47.898890image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
65773.456711
 
0.1%
31061.450891
 
0.1%
55270.91711
 
0.1%
65723.256761
 
0.1%
48722.73311
 
0.1%
48288.848651
 
0.1%
47634.974641
 
0.1%
29862.176471
 
0.1%
26998.246231
 
0.1%
73529.361221
 
0.1%
Other values (672)672
98.5%
ValueCountFrequency (%)
6690.5471121
0.1%
9629.3743961
0.1%
10176.706981
0.1%
13352.888891
0.1%
13411.633481
0.1%
14818.539061
0.1%
15259.6251
0.1%
15865.943821
0.1%
16124.05571
0.1%
16253.069311
0.1%
ValueCountFrequency (%)
79272.12451
0.1%
77371.252751
0.1%
76453.669231
0.1%
76110.1251
0.1%
75639.627661
0.1%
75595.653231
0.1%
73529.361221
0.1%
73384.100231
0.1%
73271.699191
0.1%
73259.464621
0.1%

No_transaction
Real number (ℝ≥0)

HIGH CORRELATION

Distinct356
Distinct (%)52.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean280.8739003
Minimum49
Maximum675
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.5 KiB
2022-04-13T14:53:48.039516image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum49
5-th percentile99.05
Q1173.25
median250.5
Q3392
95-th percentile533
Maximum675
Range626
Interquartile range (IQR)218.75

Descriptive statistics

Standard deviation136.8920234
Coefficient of variation (CV)0.4873789386
Kurtosis-0.4993678567
Mean280.8739003
Median Absolute Deviation (MAD)97
Skewness0.5778148295
Sum191556
Variance18739.42607
MonotonicityNot monotonic
2022-04-13T14:53:48.180145image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3036
 
0.9%
1496
 
0.9%
1966
 
0.9%
2746
 
0.9%
1775
 
0.7%
1285
 
0.7%
1615
 
0.7%
1925
 
0.7%
3395
 
0.7%
1985
 
0.7%
Other values (346)628
92.1%
ValueCountFrequency (%)
491
0.1%
571
0.1%
591
0.1%
631
0.1%
651
0.1%
721
0.1%
732
0.3%
741
0.1%
751
0.1%
762
0.3%
ValueCountFrequency (%)
6751
0.1%
6651
0.1%
6491
0.1%
6431
0.1%
6371
0.1%
6341
0.1%
6331
0.1%
6281
0.1%
6111
0.1%
6091
0.1%

Card_type
Categorical

Distinct4
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size5.5 KiB
No
512 
classic
133 
junior
 
21
gold
 
16

Length

Max length7
Median length2
Mean length3.14516129
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No512
75.1%
classic133
 
19.5%
junior21
 
3.1%
gold16
 
2.3%

Length

2022-04-13T14:53:48.305141image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-13T14:53:48.383271image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
no512
75.1%
classic133
 
19.5%
junior21
 
3.1%
gold16
 
2.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

No_inhabitants
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct77
Distinct (%)11.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean272052.2361
Minimum42821
Maximum1204953
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.5 KiB
2022-04-13T14:53:48.477019image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum42821
5-th percentile51428
Q192084
median124605
Q3226122
95-th percentile1204953
Maximum1204953
Range1162132
Interquartile range (IQR)134038

Descriptive statistics

Standard deviation358331.9752
Coefficient of variation (CV)1.317144017
Kurtosis2.745126016
Mean272052.2361
Median Absolute Deviation (MAD)45747
Skewness2.104797798
Sum185539625
Variance1.284018044 × 1011
MonotonicityNot monotonic
2022-04-13T14:53:48.632558image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
120495384
 
12.3%
38757024
 
3.5%
28538724
 
3.5%
32387020
 
2.9%
19709917
 
2.5%
22884816
 
2.3%
22612214
 
2.1%
13901214
 
2.1%
5142814
 
2.1%
8585213
 
1.9%
Other values (67)442
64.8%
ValueCountFrequency (%)
428218
1.2%
457148
1.2%
513138
1.2%
5142814
2.1%
539218
1.2%
584002
 
0.3%
587967
1.0%
672989
1.3%
706466
0.9%
706993
 
0.4%
ValueCountFrequency (%)
120495384
12.3%
38757024
 
3.5%
32387020
 
2.9%
28538724
 
3.5%
22884816
 
2.3%
22612214
 
2.1%
19709917
 
2.5%
1820278
 
1.2%
1776868
 
1.2%
1704496
 
0.9%

Average_salary
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct76
Distinct (%)11.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9502.986804
Minimum8110
Maximum12541
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.5 KiB
2022-04-13T14:53:48.788807image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum8110
5-th percentile8240
Q18544
median8991
Q39897
95-th percentile12541
Maximum12541
Range4431
Interquartile range (IQR)1353

Descriptive statistics

Standard deviation1323.150982
Coefficient of variation (CV)0.1392352751
Kurtosis0.6849555243
Mean9502.986804
Median Absolute Deviation (MAD)601
Skewness1.332946199
Sum6481037
Variance1750728.521
MonotonicityNot monotonic
2022-04-13T14:53:48.929432image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1254184
 
12.3%
989724
 
3.5%
1017724
 
3.5%
1067320
 
2.9%
962417
 
2.5%
989316
 
2.3%
899414
 
2.1%
836314
 
2.1%
840214
 
2.1%
896513
 
1.9%
Other values (66)442
64.8%
ValueCountFrequency (%)
81106
0.9%
81144
 
0.6%
81738
1.2%
818712
1.8%
82083
 
0.4%
82407
1.0%
82548
1.2%
82887
1.0%
836314
2.1%
836911
1.6%
ValueCountFrequency (%)
1254184
12.3%
112775
 
0.7%
107876
 
0.9%
1067320
 
2.9%
104465
 
0.7%
1017724
 
3.5%
101245
 
0.7%
100458
 
1.2%
99206
 
0.9%
989724
 
3.5%

Average_unemployment_rate
Real number (ℝ≥0)

HIGH CORRELATION

Distinct76
Distinct (%)11.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.192917889
Minimum0.36
Maximum8.37
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.5 KiB
2022-04-13T14:53:49.070057image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.36
5-th percentile0.36
Q11.78
median3.09
Q34.295
95-th percentile7.19
Maximum8.37
Range8.01
Interquartile range (IQR)2.515

Descriptive statistics

Standard deviation1.98404887
Coefficient of variation (CV)0.6213905083
Kurtosis-0.3700318927
Mean3.192917889
Median Absolute Deviation (MAD)1.31
Skewness0.4705602496
Sum2177.57
Variance3.936449917
MonotonicityNot monotonic
2022-04-13T14:53:49.210677image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.3684
 
12.3%
1.7824
 
3.5%
7.1924
 
3.5%
5.09520
 
2.9%
1.95517
 
2.5%
4.40516
 
2.3%
4.29514
 
2.1%
3.00514
 
2.1%
3.55514
 
2.1%
7.65513
 
1.9%
Other values (66)442
64.8%
ValueCountFrequency (%)
0.3684
12.3%
0.5211
 
1.6%
0.555
 
0.7%
0.979
 
1.3%
1.1151
 
0.1%
1.1752
 
0.3%
1.3311
 
1.6%
1.3455
 
0.7%
1.5658
 
1.2%
1.57510
 
1.5%
ValueCountFrequency (%)
8.375
 
0.7%
7.65513
1.9%
7.1924
3.5%
7.0559
 
1.3%
6.786
 
0.9%
6.684
 
0.6%
6.166
 
0.9%
5.756
 
0.9%
5.736
 
0.9%
5.524
 
0.6%

Average_crime_rate
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct77
Distinct (%)11.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.03603794572
Minimum0.01474440334
Maximum0.07667684964
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.5 KiB
2022-04-13T14:53:49.351306image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.01474440334
5-th percentile0.01734219529
Q10.0219757665
median0.03166882101
Q30.04270287024
95-th percentile0.07667684964
Maximum0.07667684964
Range0.0619324463
Interquartile range (IQR)0.02072710374

Descriptive statistics

Standard deviation0.01831162415
Coefficient of variation (CV)0.5081206428
Kurtosis0.3181398925
Mean0.03603794572
Median Absolute Deviation (MAD)0.01030393443
Skewness1.165048596
Sum24.57787898
Variance0.0003353155789
MonotonicityNot monotonic
2022-04-13T14:53:49.491934image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0766768496484
 
12.3%
0.0482712800324
 
3.5%
0.0350156103824
 
3.5%
0.0573208386120
 
2.9%
0.0221944302117
 
2.5%
0.0251476962916
 
2.3%
0.0417473753114
 
2.1%
0.0192897016114
 
2.1%
0.0203974488614
 
2.1%
0.0318862693913
 
1.9%
Other values (67)442
64.8%
ValueCountFrequency (%)
0.014744403344
0.6%
0.015856705828
1.2%
0.016018306649
1.3%
0.017284951367
1.0%
0.017339714527
1.0%
0.01738933017
1.0%
0.017465583217
1.0%
0.017689169366
0.9%
0.018659491628
1.2%
0.018668148919
1.3%
ValueCountFrequency (%)
0.0766768496484
12.3%
0.0573208386120
 
2.9%
0.053938556646
 
0.9%
0.050214917833
 
0.4%
0.0482712800324
 
3.5%
0.048218527326
 
0.9%
0.0463000912311
 
1.6%
0.045357604965
 
0.7%
0.044122757485
 
0.7%
0.042702870248
 
1.2%

gender
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.5 KiB
female
348 
male
334 

Length

Max length6
Median length6
Mean length5.020527859
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowmale
2nd rowmale
3rd rowmale
4th rowfemale
5th rowmale

Common Values

ValueCountFrequency (%)
female348
51.0%
male334
49.0%

Length

2022-04-13T14:53:49.632560image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-13T14:53:49.710647image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
female348
51.0%
male334
49.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Owner_age
Real number (ℝ≥0)

Distinct391
Distinct (%)57.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean457.2800587
Minimum162
Maximum742
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.5 KiB
2022-04-13T14:53:49.804433image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum162
5-th percentile220
Q1326.5
median451.5
Q3587
95-th percentile693
Maximum742
Range580
Interquartile range (IQR)260.5

Descriptive statistics

Standard deviation153.3584071
Coefficient of variation (CV)0.3353708613
Kurtosis-1.129721745
Mean457.2800587
Median Absolute Deviation (MAD)129.5
Skewness0.0191246409
Sum311865
Variance23518.80104
MonotonicityNot monotonic
2022-04-13T14:53:49.929433image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6446
 
0.9%
3826
 
0.9%
3626
 
0.9%
5355
 
0.7%
6035
 
0.7%
2395
 
0.7%
5615
 
0.7%
2765
 
0.7%
2845
 
0.7%
5604
 
0.6%
Other values (381)630
92.4%
ValueCountFrequency (%)
1621
0.1%
1701
0.1%
1711
0.1%
1751
0.1%
1761
0.1%
1791
0.1%
1831
0.1%
1861
0.1%
1872
0.3%
1901
0.1%
ValueCountFrequency (%)
7421
 
0.1%
7412
0.3%
7351
 
0.1%
7342
0.3%
7311
 
0.1%
7291
 
0.1%
7281
 
0.1%
7271
 
0.1%
7241
 
0.1%
7223
0.4%

Same_district
Boolean

HIGH CORRELATION

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size810.0 B
True
622 
False
 
60
ValueCountFrequency (%)
True622
91.2%
False60
 
8.8%
2022-04-13T14:53:50.023186image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Default
Boolean

HIGH CORRELATION

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size810.0 B
True
434 
False
248 
ValueCountFrequency (%)
True434
63.6%
False248
36.4%
2022-04-13T14:53:50.070061image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Interactions

2022-04-13T14:53:43.231887image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T14:53:20.300436image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T14:53:22.564877image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T14:53:24.505514image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T14:53:26.307915image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T14:53:28.396996image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T14:53:30.131399image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T14:53:31.990746image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T14:53:33.998881image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T14:53:35.967669image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T14:53:37.702042image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T14:53:39.417233image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T14:53:41.522672image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T14:53:43.372518image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T14:53:20.861871image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T14:53:22.721158image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T14:53:24.630519image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T14:53:26.448569image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T14:53:28.522001image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T14:53:30.272023image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T14:53:32.131403image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T14:53:34.123917image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T14:53:36.108292image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T14:53:37.811419image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T14:53:39.568495image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T14:53:41.663290image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T14:53:43.500177image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T14:53:21.018155image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T14:53:22.861754image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T14:53:24.771138image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T14:53:26.575984image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T14:53:28.647027image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T14:53:30.397032image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T14:53:32.256408image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T14:53:34.264534image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T14:53:36.233284image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T14:53:37.952043image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T14:53:39.694511image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T14:53:41.788291image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T14:53:43.640798image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T14:53:21.143152image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T14:53:23.002412image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T14:53:24.896138image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T14:53:26.700979image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T14:53:28.772033image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T14:53:30.537619image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T14:53:32.397030image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T14:53:34.655132image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T14:53:36.358295image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T14:53:38.061411image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T14:53:39.835137image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T14:53:41.913293image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T14:53:43.750166image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T14:53:21.299394image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T14:53:23.127411image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T14:53:25.036738image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T14:53:26.841597image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T14:53:28.912651image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T14:53:30.693871image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T14:53:32.537655image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T14:53:34.780160image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T14:53:36.498911image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T14:53:38.186411image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T14:53:40.007012image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T14:53:42.038298image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T14:53:43.875183image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T14:53:21.440005image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T14:53:23.268032image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T14:53:25.161764image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T14:53:26.982223image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T14:53:29.037648image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T14:53:30.834523image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T14:53:32.758487image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T14:53:34.905160image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T14:53:36.623920image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T14:53:38.327022image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T14:53:40.132040image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T14:53:42.185014image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T14:53:44.015802image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T14:53:21.580673image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T14:53:23.424253image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T14:53:25.307442image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T14:53:27.122850image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T14:53:29.178245image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T14:53:30.975154image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T14:53:32.920153image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T14:53:35.045795image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T14:53:36.780160image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T14:53:38.452006image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T14:53:40.272680image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T14:53:42.310022image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T14:53:44.156469image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T14:53:21.739659image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T14:53:23.599232image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T14:53:25.511070image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T14:53:27.263474image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T14:53:29.318908image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T14:53:31.115779image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T14:53:33.076396image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T14:53:35.186422image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T14:53:36.905159image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T14:53:38.577044image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T14:53:40.428922image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T14:53:42.450655image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T14:53:44.281416image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T14:53:21.864603image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T14:53:23.755523image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T14:53:25.651665image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T14:53:27.404070image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T14:53:29.459529image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T14:53:31.256428image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T14:53:33.255038image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T14:53:35.311421image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T14:53:37.045791image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T14:53:38.717660image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T14:53:40.600793image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T14:53:42.591264image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T14:53:44.406421image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T14:53:22.005264image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T14:53:23.911788image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T14:53:25.776673image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T14:53:27.794732image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T14:53:29.600152image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T14:53:31.397042image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T14:53:33.417774image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T14:53:35.452008image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T14:53:37.170795image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T14:53:38.842632image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T14:53:40.741423image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T14:53:42.716262image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T14:53:44.515802image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T14:53:22.130263image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T14:53:24.052389image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T14:53:25.901670image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T14:53:27.935349image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T14:53:29.725147image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T14:53:31.553300image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T14:53:33.550611image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T14:53:35.577037image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T14:53:37.295815image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T14:53:38.967631image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T14:53:40.866419image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T14:53:42.841271image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T14:53:44.656421image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T14:53:22.270881image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T14:53:24.208614image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T14:53:26.042262image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T14:53:28.082149image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T14:53:29.865746image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T14:53:31.693920image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T14:53:33.709883image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T14:53:35.717659image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T14:53:37.436396image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T14:53:39.139507image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T14:53:41.257059image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T14:53:42.981859image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T14:53:44.781424image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T14:53:22.401178image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T14:53:24.349270image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T14:53:26.182887image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T14:53:28.256374image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T14:53:30.006369image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T14:53:31.850154image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T14:53:33.864313image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T14:53:35.842669image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T14:53:37.561430image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T14:53:39.280132image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T14:53:41.397672image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-13T14:53:43.122518image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Correlations

2022-04-13T14:53:50.148180image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-04-13T14:53:50.429402image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-04-13T14:53:50.695027image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-04-13T14:53:50.945029image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-04-13T14:53:51.132528image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-04-13T14:53:45.031419image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
A simple visualization of nullity by column.
2022-04-13T14:53:45.345067image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

account_idamountdurationpaymentsDIDAverage_order_amountAverage_trans_amountAverage_trans_balanceNo_transactionCard_typeNo_inhabitantsAverage_salaryAverage_unemployment_rateAverage_crime_rategenderOwner_ageSame_districtDefault
0527079608243317.03133317.00000015016.19480565773.456710462No10560682543.2750.021263male282.0TrueFalse
11126552788124399.02442504.0000002654.84504122010.194215484No5879690453.3650.031669male246.0TrueFalse
2103642192436609.02973435.5000004808.55852246258.338809487No15704287432.1550.024048male398.0TrueFalse
3383423052121921.06171772.2000003333.60150424129.730827665No38757098971.7800.048271female646.0TrueFalse
4930741904123492.06033626.8333337649.58859858766.853621649No22884898934.4050.025148male242.0FalseFalse
5589165184125432.04485432.30000011066.31896670233.519397464gold38757098971.7800.048271male438.0FalseFalse
6647376908126409.04856408.80000011575.71670246888.488372473No10787087544.0700.035561female580.0FalseTrue
71843105804362939.01854966.3500005983.71645035669.385281462classic10787087544.0700.035561female639.0FalseFalse
8926539576123298.05225602.7666678694.99533464095.143079643junior1204953125410.3600.076677female170.0FalseFalse
98051208320484340.04794340.0000009533.83173141532.387019416No1204953125410.3600.076677female313.0FalseFalse

Last rows

account_idamountdurationpaymentsDIDAverage_order_amountAverage_trans_amountAverage_trans_balanceNo_transactionCard_typeNo_inhabitantsAverage_salaryAverage_unemployment_rateAverage_crime_rategenderOwner_ageSame_districtDefault
672105352704487348.05137348.0008368.84745830200.62711959classic10334791041.7900.022512female575.0TrueTrue
67311317317460605291.04993974.60013987.63225866468.587097155No10260981875.1400.020744male276.0TrueTrue
6743293276084367669.04762349.14010670.81290359131.412903155classic1204953125410.3600.076677female416.0TrueTrue
67537318480605308.04222576.3757293.53076937547.484615130No7064685473.1450.021976male553.0TrueFalse
67691401603248334.04492418.2503636.25185222821.851852135No10505892723.0100.042096female395.0TrueTrue
677569899216362756.04436438.00012802.79798058031.55555699No1204953125410.3600.076677female572.0TrueTrue
678650538496123208.03903207.8007720.36734745556.53061249classic9472599202.5650.048219female416.0TrueTrue
6799156163332364537.04434537.3008251.59722228793.45833372No7069989683.0900.025814male731.0TrueTrue
680276160920364470.03594841.25011168.21538550293.47692365classic8585289657.6550.031886male325.0TrueTrue
6811318185544365154.02933670.4006633.82456122717.73684257No12460587724.8350.024124male708.0TrueTrue